提高发电量:采用综合分析和人工智能优化标准的新型氢基方案

Energy Storage Pub Date : 2024-08-22 DOI:10.1002/est2.70013
Vahid Mohammadzadeh, Zoheir Saboohi, Fathollah Ommi, Ehsan Gholamian
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引用次数: 0

摘要

尽管质子交换膜燃料电池(PEMFC)是一项尖端技术,但它在工作时会产生大量热量,从而造成能源浪费。为了通过余热回收提高能源效率,我们提供并分析了一种利用 PEMFC 和 ORC 技术的新型集成能源系统。将燃料电池(FC)产生的废热加以充分利用的方法有很多,但最有效的方法是采用合适工作流体的有机朗肯循环(ORC)。这项研究旨在通过测试几种工作流体,找到利用燃料电池余热的最佳方法。通过监测表征系统整体性能的目标函数在不同系统参数下的变化情况,利用遗传算法得出最佳解决方案。结果表明,所建议的高效集成实现了较高的能效和放能效水平,并将总成本和环境影响控制在可接受的范围内。由于燃料使用要素的内容会在多个方面对系统指标产生重大影响,因此结果也证明了它的相关性。由于能量环境指标和能量效率指标总是朝着不同的方向发展,因此选择一个满足多项要求的设计条件至关重要。结果显示,燃料电池的不可逆率最高,为 12.2,是最节能的设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Power Production: A Novel Hydrogen-Based Scheme With Comprehensive Analysis and AI-Optimized Criteria

Despite being a cutting-edge technology, the proton exchange membrane fuel cell (PEMFC) generates a lot of heat as it works, which makes it wasteful with energy. In order to enhance energy efficiency via waste heat recovery, we provide and analyze a novel integrated energy system that utilizes PEMFC and ORC technology. There are a lot of ways to put the waste heat from fuel cells (FCs) to good use, but the most efficient one is the organic Rankine cycle (ORC) with the right working fluid. This research aims to find the optimal way to use the waste heat of the FC by testing several working fluids. The optimal solution is derived using a genetic algorithm by monitoring the objective functions that characterize the system's overall performance as they vary across different system parameters. The results show that the proposed efficient integration achieves high energy and exergy efficiency levels and achieves rates of total cost and environmental impact that are within acceptable limits. Since the fuel usage element's content significantly affects the system indicators in several ways, the results also demonstrate that it is quite relevant. Since the exergo-environmental metric and the exergy efficiency meter are always moving in different directions, choosing a design condition that meets several requirements is crucial. According to the results, fuel cells had the highest irreversibility rate at 12.2, making them the most energy-conserving piece of machinery.

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